{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sampling time analysis scratchpad\n",
    "\n",
    "Attempts to analyse the sampling time based on the timestamps associated during acquisition.\n",
    "Acquisition timestamps are susceptible to jitter and using the RawData example does not account for filtering overhead."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from matplotlib import pyplot as plt\n",
    "import pandas as pd\n",
    "from scipy import signal\n",
    "import numpy as np\n",
    "import recorder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:recorder:Starting streamer thread, delaying acquisition by 5.00s\n",
      "INFO:recorder:Gathered 0 samples\n",
      "INFO:recorder:Gathered 0 samples\n",
      "INFO:recorder:Gathered 0 samples\n",
      "INFO:recorder:Gathered 0 samples\n",
      "INFO:recorder:Gathered 0 samples\n",
      "INFO:recorder:Started acquisition\n",
      "INFO:recorder:Gathered 1 samples\n",
      "INFO:recorder:Gathered 98 samples\n",
      "INFO:recorder:Gathered 196 samples\n",
      "INFO:recorder:Gathered 294 samples\n",
      "INFO:root:Acquired 294 samples (requested: 200), stopping\n",
      "INFO:recorder:Stopping streamer thread\n"
     ]
    }
   ],
   "source": [
    "# Record 200 samples worth of data, delaying acquisition by 5 seconds\n",
    "# this can be done also using the command line interface of recorder.py\n",
    "recorder.run(port='/dev/ttyACM0', outfile='test.out', holdoff=5, debug=False, samples=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Read from the recorded session\n",
    "df = pd.read_csv('test.out', delimiter='\\t', index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_ts = pd.DataFrame(df.index)\n",
    "df_ts_diffs = df_ts.diff()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f722ad48450>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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gn7cOBdjnv7yswjghennboacN/s6djeQ1H+V1fcWwQ3HpJ/7r+c/M2F8KNDvb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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f722ad7aa90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_ts_diffs[10:40].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f7228417890>]], dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f726808bb50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_ts_diffs.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
